ccdv/pubmed-summarization
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A production-ready Mixture of Experts (MoE) model optimized for medical Q&A, built with safety-first principles and ethical AI guidelines.
This model is designed for medical question-answering applications, providing:
The model was trained on verified medical datasets:
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load model and tokenizer
model_name = "OsamaBinLikhon/SheikhNextMoE-Medical-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Medical query
prompt = "### Patient: What are symptoms of diabetes?
### Medical Assistant:"
inputs = tokenizer(prompt, return_tensors="pt")
# Generate response
outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
⚠️ Important: This model provides medical information for educational purposes only. It is not a substitute for professional medical advice, diagnosis, or treatment. Always consult qualified healthcare providers for medical concerns.
Apache 2.0 - See LICENSE file for details.
@misc{SheikhNextMoE,
title={SheikhNextMoE: Ethical Medical Assistant},
author={MiniMax Agent},
year={2025},
url={https://huggingface.co/OsamaBinLikhon/SheikhNextMoE-Medical-v2}
}